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my_awesome_model

Text Classification
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bert
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text-embeddings-inference
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End of training

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  1. README.md +10 -10
README.md CHANGED
@@ -5,6 +5,7 @@ tags:
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  - generated_from_trainer
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  metrics:
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  - accuracy
 
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  model-index:
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  - name: my_awesome_model
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  results: []
@@ -17,8 +18,9 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0019
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- - Accuracy: 1.0
 
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  ## Model description
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  - seed: 42
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  - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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  - lr_scheduler_type: linear
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- - num_epochs: 5
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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- |:-------------:|:-----:|:----:|:---------------:|:--------:|
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- | No log | 1.0 | 14 | 0.0790 | 0.9864 |
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- | No log | 2.0 | 28 | 0.0517 | 0.9773 |
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- | No log | 3.0 | 42 | 0.0055 | 1.0 |
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- | No log | 4.0 | 56 | 0.0026 | 1.0 |
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- | No log | 5.0 | 70 | 0.0019 | 1.0 |
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  ### Framework versions
 
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  - generated_from_trainer
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  metrics:
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  - accuracy
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+ - f1
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  model-index:
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  - name: my_awesome_model
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  results: []
 
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  This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.2001
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+ - Accuracy: 0.9464
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+ - F1: 0.9403
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  ## Model description
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  - seed: 42
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  - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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  - lr_scheduler_type: linear
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+ - num_epochs: 3
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
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+ | No log | 1.0 | 66 | 0.2467 | 0.9272 | 0.9201 |
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+ | No log | 2.0 | 132 | 0.2089 | 0.9464 | 0.9400 |
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+ | No log | 3.0 | 198 | 0.2001 | 0.9464 | 0.9403 |
 
 
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  ### Framework versions